Towards accurate visual information estimation with Entropy of Primitive

Recently, a novel concept referred to as Entropy of Primitive (EoP) has been proposed for evaluating the visual information of natural images. The idea originates from the sparse representation, which has been successfully applied in a wide variety of signal processing and analysis tasks. This is because of the high efficiency of sparse representation in dealing with rich, varied and directional information contained in the natural scene. In this paper, we further explore the EoP to bridge the sparse representation and visual perception. Sparse primitives are divided into three categories depending on their visual importance. Accordingly the visual signal is decomposed into structural and non-structural layers. It is found that the image sparse representation is highly relevant with the hierarchical visual information construction process in representing the natural scene. We evaluate the efficiency and robustness of the EoP in real applications, including surveillance video and shot boundary detection.